Improving Object Detection Quality by Incorporating Global Contexts via Self-Attention

Attention-based Object Detection

Improving predictive models of glaucoma severity by incorporating quality indicators

OBJECTIVE In this paper we present an evaluation of the role of reliability indicators in glaucoma severity prediction. In particular, we investigate whether it is possible to extract useful information from tests that would be normally discarded because they are considered unreliable. METHODS We set up a predictive modelling framework to predict glaucoma severity from visual field (VF) tests...

Object detection by global contour shape

We present a method for object class detection in images based on global shape. A distance measure for elastic shape matching is derived, which is invariant to scale and rotation, and robust against non-parametric deformations. Starting from an over-segmentation of the image, the space of potential object boundaries is explored to find boundaries, which have high similarity with the shape templ...

Reducing Medical Costs and Improving Quality via Self-Management Tools

April 2007 | Volume 4 | Issue 4 | e104 In 2004, health-care providers in the United States consumed $US1.9 trillion, or about 16% of the gross domestic product. By 2010, the cost of health care is predicted to exceed 20% of the US gross domestic product [1]. The management of chronic diseases currently accounts for 70%–75% of health-care spending [2], and this proportion is likely to increase i...

Incorporating Global Visual Features into Attention-based Neural Machine Translation

We introduce multi-modal, attentionbased Neural Machine Translation (NMT) models which incorporate visual features into different parts of both the encoder and the decoder. Global image features are extracted using a pre-trained convolutional neural network and are incorporated (i) as words in the source sentence, (ii) to initialise the encoder hidden state, and (iii) as additional data to init...